Title

Automated Differential Diagnosis of Early Parkinsonism using Metabolic Brain Networks: A Validation Study

Publication Date

2016

Journal Title

J Nucl Med

Abstract

The differentiation of idiopathic Parkinson's disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian "look-alike" syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations. In an initial study (Tang et al., Lancet Neurol 2010), we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based upon the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered. METHODS: We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 years or less at the time of imaging. After imaging, subjects were followed by a blinded movement disorders specialist for an average of 2.2 years before a final diagnosis was made. By applying the algorithm to the individual scan data, the probabilities of IPD, MSA and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared to the final clinical diagnosis. RESULTS: Using the original two-level logistical classification algorithm, IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV). The algorithm achieved 90% specificity and 85% PPV for MSA, and 94% specificity and 94% PPV for PSP. Diagnostic accuracy was similarly high (specificity and PPV > 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as Level-I Indeterminate Parkinsonism and four more subjects as Level-II Indeterminate APS. CONCLUSION: Automated pattern-based image classification can improve diagnostic accuracy in patients with parkinsonism, even at early disease stages.

Volume Number

57

Issue Number

1

Pages

60-6

Document Type

Article

EPub Date

2015/10/10

Status

Faculty; Northwell Researcher

Facility

School of Medicine; Northwell Health

Primary Department

Molecular Medicine

Additional Departments

Neurology

PMID

26449840

DOI

10.2967/jnumed.115.161992